library("tidyr")
library('ggplot2')
library('dplyr')

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library("glue")
library('ggVennDiagram')

wkdir = "~/Desktop/GitHub/Obesity/NewExtractions/H9N2/timo_0.01"
setwd(wkdir)
savedir = "~/Desktop/GitHub/Obesity/NewExtractions/H9N2/timo_0.01/Output_Figures"

source("~/Desktop/GitHub/Obesity/NewExtractions/H9N2/FD_functions.R")
diet = c("Obese","Lean","Control")
dietColors = c("#FF9933","#66CCFF","#606060")
names(dietColors) = diet
DietcolScale_fill <- scale_fill_manual(name = "grp",values = dietColors)
DietcolScale <- scale_colour_manual(name = "grp",values = dietColors)

Specifying thresholds and plotting variables

cov_cut = 200
freq_cut = 0.01
pvalcut  = 0.05

ntlist = c("A","C","G","T")
SEGMENTS = c('H9N2_PB2','H9N2_PB1','H9N2_PA','H9N2_HA','H9N2_NP','H9N2_NA','H9N2_MP','H9N2_NS')

#Loading metadata This includes titer and Ct values when applicable. ND indicates qPCR was run with a negative result; 0 indicates plaque assay or HAI was run with a negative result. NA for any values indicate that data was missing. Sacrificed indicates there was no data at that time point because the ferret had already been sacrficied for pathology.

metafile = metafile = "~/Desktop/GitHub/Obesity/NewExtractions/H9N2/H9_Metadata.csv"

meta = read.csv(file=metafile,header=T,sep=",",na.strings = c(''))
meta = filter(meta, resequenced == "yes")

meta$Ct_Mgene = as.numeric(meta$Ct_Mgene)
Warning: NAs introduced by coercion
meta$titer = as.numeric(meta$titer)
Warning: NAs introduced by coercion
meta$log10_titer = as.numeric(meta$log10_titer)
Warning: NAs introduced by coercion
meta$inf_route = factor(meta$inf_route, levels = c("Index","Contact","Aerosol","Control"))

Loading in coverage file & segment size information

cov = read.csv("./avg_coverage/H9N2.coverage.csv", header = TRUE, sep = ",")

seg_sizes = "../SegmentSize.csv"
sizes = read.csv(file=seg_sizes,header=T,sep=",",na.strings = c(''))
GenomeSize = (sizes %>% filter(segment == 'H9N2_GENOME'))$SegmentSize

cov$segment = factor(cov$segment, levels = SEGMENTS)

Checking if data passes thresholds

cov_check = CoverageAcross(cov,cov_cut,40,sizes, wkdir)
Coverage cutoff is: 200x
Percentage covered cutoff is: 40%

Merging coverage check info with the rest of the metadata

meta = merge(meta, cov_check, by.x = c("sample"), by.y = c("name"), all.y = TRUE)

nrow(meta)
[1] 1624
count(meta,quality)

Loading in variant files

varfile = "./varfiles/H9N2.VariantsOnly.0.01.200.csv"

# read and rearrange the data
vars = read.csv(file=varfile,header=T,sep=",",na.strings = c(''))
vars$name = vars$sample

Rearranging variant dataframe

vdf = ArrangeVarWRep(vars)
# already have replicate data in the varfiles from running CompareReps.v2.py script
vdf = vdf[!duplicated(vdf), ] %>% droplevels()
nrow(vdf)
[1] 1781

Filtering variant df by timo binocheck

vdf$binocheck = factor(vdf$binocheck, levels = c("False","R1","R2","True"))
vdf_bino = filter(vdf, binocheck != "False")
vdf_bino = vdf_bino[!duplicated(vdf_bino), ] %>% droplevels()
nrow(vdf_bino)
[1] 1166
# this really gets rid of a lot of variants (~1000)

Filtering variant df with frequency cutoffs

vdf = filter(vdf, minorfreq1 >= freq_cut & 
               minorfreq2 >= freq_cut & 
               minor %in% ntlist &
               major %in% ntlist) %>% 
            droplevels()
# based on MAF study, reps and 0.01% cutoff was best combo
#filter each replicate separately rather than using the average

vdf = vdf[!duplicated(vdf), ] %>% droplevels()
nrow(vdf)
[1] 1702
# does not eliminate any variants here

Adding metadata

vdf = merge(vdf,meta, by = c("sample","segment"))
vdf = vdf[!duplicated(vdf), ] %>% droplevels()

vdf$segment = factor(vdf$segment, levels = SEGMENTS)

vdf = filter(vdf, inf_route == "Index" | inf_route == "Contact" | inf_route == "Control")
# ignoring aerosol for now
vdf = filter(vdf, quality == "good")
vdf = vdf[!duplicated(vdf), ] %>% droplevels()

good_names = c(levels(factor(vdf$sample)))
transmission_info = "/Users/marissaknoll/Desktop/GitHub/Obesity/NewExtractions/H9N2/TransmissionPairs.csv"
pairs = read.csv(transmission_info, header = T)
con_change = filter(vdf, stocknt != major) %>%
  filter(major %in% ntlist)
con_change = con_change[!duplicated(con_change), ]
con_change$ntvar = paste0(con_change$ferretID,"_",con_change$segment,"_",
                        con_change$major,"_",con_change$ntpos,"_",con_change$minor)
consensus = unique(con_change$ntvar)
length(consensus)
[1] 13
vdf$ntvar = paste0(vdf$ferretID,"_",vdf$segment,"_",vdf$major,"_",vdf$ntpos,"_",vdf$minor)

minorvdf = filter(vdf, !(ntvar %in% consensus)) %>% unique()
nrow(vdf) - nrow(minorvdf)
[1] 18

SNV location plots

SNVLocation = ggplot(minorvdf, aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet, shape = cohort)) +
  facet_grid(inf_route~segment) +
  PlotTheme1 +
  DietcolScale
print(SNVLocation)
ggsave(SNVLocation, file = "SNVLocation.pdf", path = savedir)
Saving 7.29 x 4.51 in image

# ferret 1787 doesn't have any variants??
minorvdf$ntvar = paste0(minorvdf$segment,"_",minorvdf$major,minorvdf$ntpos,minorvdf$minor)

# Comparing to SNVs found in the stock

F17_stock = filter(minorvdf, DPI == "Stock", cohort == "F17") 
F17_stock_ntvar = unique(F17_stock$ntvar)
W17_stock = filter(minorvdf, DPI == "Stock", cohort == "W17")
W17_stock_ntvar = unique(W17_stock$ntvar)
Sm18_stock = filter(minorvdf, DPI == "Stock", cohort == "Sm18")
Sm18_stock_ntvar = unique(Sm18_stock$ntvar)
Sp19_stock = filter(minorvdf, DPI == "Stock", cohort == "Sp19")
Sp19_stock_ntvar = unique(Sp19_stock$ntvar)
Sp20_stock = filter(minorvdf, DPI == "Stock", cohort == "Sp20")
Sp20_stock_ntvar = unique(Sp20_stock$ntvar)

F17_ferret = filter(minorvdf , cohort == "F17", inf_route != "Control")
F17_ferret_ntvar = unique(F17_ferret$ntvar)
W17_ferret = filter(minorvdf ,cohort == "W17", inf_route != "Control")
W17_ferret_ntvar = unique(W17_ferret$ntvar)
Sm18_ferret = filter(minorvdf ,cohort == "Sm18", inf_route != "Control")
Sm18_ferret_ntvar = unique(Sm18_ferret$ntvar)
Sp19_ferret = filter(minorvdf ,cohort == "Sp19", inf_route != "Control")
Sp19_ferret_ntvar = unique(Sp19_ferret$ntvar)
Sp20_ferret = filter(minorvdf ,cohort == "Sp20", inf_route != "Control")
Sp20_ferret_ntvar = unique(Sp20_ferret$ntvar)
F17_shared = F17_ferret %>% filter(ntvar %in% F17_stock_ntvar) %>% filter((ntvar %in% F17_ferret_ntvar)) %>% unique()
F17_denovo = F17_ferret %>% filter((ntvar %in% F17_ferret_ntvar)) %>% filter(!(ntvar %in% F17_stock_ntvar)) %>% unique()

W17_shared = W17_ferret %>% filter(ntvar %in% W17_stock_ntvar) %>% filter((ntvar %in% W17_ferret_ntvar)) %>% unique()
W17_denovo = W17_ferret %>% filter((ntvar %in% W17_ferret_ntvar)) %>% filter(!(ntvar %in% W17_stock_ntvar)) %>% unique()

Sm18_shared = Sm18_ferret %>% filter(ntvar %in% Sm18_stock_ntvar) %>% filter((ntvar %in% Sm18_ferret_ntvar)) %>% unique()
Sm18_denovo = Sm18_ferret %>% filter((ntvar %in% Sm18_ferret_ntvar)) %>% filter(!(ntvar %in% Sm18_stock_ntvar)) %>% unique()

Sp19_shared = Sp19_ferret %>% filter(ntvar %in% Sp19_stock_ntvar) %>% filter((ntvar %in% Sp19_ferret_ntvar)) %>% unique()
Sp19_denovo = Sp19_ferret %>% filter((ntvar %in% Sp19_ferret_ntvar)) %>% filter(!(ntvar %in% Sp19_stock_ntvar)) %>% unique()

Sp20_shared = Sp20_ferret %>% filter(ntvar %in% Sp20_stock_ntvar) %>% filter((ntvar %in% Sp20_ferret_ntvar)) %>% unique()
Sp20_denovo = Sp20_ferret %>% filter((ntvar %in% Sp20_ferret_ntvar)) %>% filter(!(ntvar %in% Sp20_stock_ntvar)) %>% unique()
stock_shared = rbind(F17_shared, W17_shared, Sm18_shared, Sp19_shared, Sp20_shared)
stock_shared$aavar = paste0(stock_shared$majoraa,stock_shared$aapos,stock_shared$minoraa)

ferunique = rbind(F17_denovo, W17_denovo, Sm18_denovo, Sp19_denovo, Sp20_denovo)
ferunique$aavar = paste0(ferunique$majoraa,ferunique$aapos,ferunique$minoraa)

SNV Location compared to stock

StockSharedPlot = ggplot(stock_shared, aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet, shape = cohort), size = 2) +
  facet_grid(inf_route~segment, drop = FALSE) +
  PlotTheme1 +
  DietcolScale +
  ggtitle("SNVs found in stock")
print(StockSharedPlot)
ggsave(StockSharedPlot, file = "StockSharedPlot.pdf", height = 30, width = 15, path = savedir)


FerUniquePlot = ggplot(ferunique, aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet)) +
  facet_grid(inf_route~segment) +
  PlotTheme1 +
  DietcolScale +
  ggtitle("SNVs not found in stock")
print(FerUniquePlot)
ggsave(FerUniquePlot, file = "FerUniquePlot.pdf", path = savedir)
Saving 7.29 x 4.51 in image

Venn diagram of obese and lean de novo SNVs

o_var = filter(ferunique, diet == "Obese") 
o_var = unique(o_var$ntvar)

l_var = filter(ferunique, diet == "Lean") 
l_var = unique(l_var$ntvar)

diet_var <- list(Obese = o_var, Lean = l_var)

DietUniqueSNVS = ggVennDiagram(diet_var)
print(DietUniqueSNVS)
ggsave(DietUniqueSNVS, file = "DietUniqueSNVS.pdf", path = savedir)
Saving 7.29 x 4.51 in image

Obese- and lean-specific SNVs

lean = ferunique %>% 
  filter(ntvar %in% l_var) %>% 
  filter(!(ntvar %in% o_var)) %>% 
  unique()

lean$ferretID_var = paste0(lean$ferretID,"_",lean$ntvar)

repeats_lean = lean %>% 
  group_by(ntvar,ferretID) %>% 
  tally() %>%
  group_by(ntvar) %>%
  tally() %>% unique()

lean = merge(lean, repeats_lean, by = c("ntvar")) %>% unique()

obese = ferunique %>% 
  filter(ntvar %in% o_var) %>% 
  filter(!(ntvar %in% l_var)) %>%
  unique()

obese$ferretID_var = paste0(obese$ferretID,"_",obese$ntvar)

repeats_obese = obese %>% 
  group_by(ntvar,ferretID) %>% 
  tally() %>%
  group_by(ntvar) %>%
  tally() %>%
  unique()

obese = merge(obese, repeats_obese, by = c("ntvar")) %>% unique()

Determining if diet-unique shared variants are transmitted

dietunique = rbind(lean,obese) %>% unique()
dietunique = merge(dietunique, pairs, by = c("ferretID"))

shared = filter(dietunique, n == 2)
t = unique(shared$ntvar)

transmitted = data.frame()

for(i in t){
  print(i)
  df = filter(shared, ntvar == i)
  df1 = group_by(df,pair_numbers) %>% tally()
  # here a 2 means that the two ferrets are in the same transmission pair and a 1 indicates different transmission pairs
  df2 = merge(df, df1, by = c("pair_numbers"))
  # add this information back into the dataframe
  df2$transmission = df2$n.y
  transmitted = rbind(transmitted, df2)
}
[1] "H9N2_NA_G452A"
[1] "H9N2_HA_A658G"
[1] "H9N2_PB2_A1351T"
[1] "H9N2_MP_G459A"
[1] "H9N2_NA_G72A"
[1] "H9N2_PB2_A480G"
[1] "H9N2_NS_A719G"
[1] "H9N2_HA_C383A"
[1] "H9N2_MP_T444C"
[1] "H9N2_PB1_T905C"
[1] "H9N2_PB2_A482G"
[1] "H9N2_NP_T911C"
[1] "H9N2_PB1_T906C"
[1] "H9N2_HA_G808A"
[1] "H9N2_HA_C802T"
[1] "H9N2_PB2_C1928G"
[1] "H9N2_PA_G1986A"
[1] "H9N2_NS_G294A"
[1] "H9N2_PB1_G738A"
[1] "H9N2_HA_G651A"
[1] "H9N2_MP_G339A"
[1] "H9N2_PB1_T1604C"
[1] "H9N2_HA_C1118T"
[1] "H9N2_HA_C375T"
[1] "H9N2_PB1_G591A"
[1] "H9N2_NS_G660A"
[1] "H9N2_PA_C1873T"
[1] "H9N2_HA_A747G"
[1] "H9N2_NP_C249T"
[1] "H9N2_PA_C1782A"
[1] "H9N2_HA_A1531T"
#formatting stuff
notshared = filter(dietunique, n == 1)
notshared$transmission = 0
transmitted$n = transmitted$n.x
transmitted = transmitted %>% select(!(n.x)) %>% select(!(n.y))

dietunique = rbind(notshared, transmitted)
dietunique$transmission = as.character(dietunique$transmission)
DietUnique = ggplot(dietunique, 
                    aes(x = ntpos,
                        y = factor(segment, levels = c('H9N2_NS','H9N2_MP','H9N2_NA','H9N2_NP','H9N2_HA','H9N2_PA','H9N2_PB1','H9N2_PB2')))) +
  geom_point(aes(color = nonsyn, size = n)) + 
  geom_text(data = filter(dietunique, n == 2, nonsyn == "nonsyn"), aes(label = aavar, vjust = 2, hjust = 0.5)) +
  ggtitle("Number of samples containing each variant - diet specific") +
  facet_grid(STRAIN~diet) +
  ylab("Segment") +
  xlab("Nucleotide Position") +
  PlotTheme1
print(DietUnique)
ggsave(DietUnique, filename = "SegmentSNVPlot_DietUnqique.pdf", path = savedir, width = 10, height = 7)

# make new version of this figure, separating out transmission v independent ferrets
DietUnique = ggplot(filter(dietunique), 
                             aes(x = ntpos, 
                                 y = factor(segment, levels = c('H9N2_NS','H9N2_MP','H9N2_NA','H9N2_NP','H9N2_HA','H9N2_PA','H9N2_PB1','H9N2_PB2')))) +
  geom_point(aes(color = nonsyn, size = n)) + 
  ggtitle("Number of samples containing each variant - diet specific") +
  xlab("Nucleotide position") +
  ylab("Segment") +
  PlotTheme1
print(DietUnique)
ggsave(DietUnique, filename = "SegmentSNVPlot_DietUnqique.pdf", path = savedir, width = 15, height = 10)



DietUnique_Transmission = ggplot(filter(dietunique, n > 1, nonsyn != "syn"), 
                             aes(x = ntpos, 
                                 y = factor(segment, levels = c('H9N2_NS','H9N2_MP','H9N2_NA','H9N2_NP','H9N2_HA','H9N2_PA','H9N2_PB1','H9N2_PB2')))) +
  geom_point(aes(color = transmission, size = 2, shape = transmission)) + 
  ggtitle("Number of samples containing each variant - diet specific") +
  xlab("Nucleotide position") +
  ylab("Segment") +
  facet_grid(diet~inf_route) +
  PlotTheme1
print(DietUnique_Transmission)
ggsave(DietUnique_Transmission, file = "DietUnique_Transmission.pdf", width = 7, height = 5, path = savedir)

Pulling out repeated nonsynonymous mutations

nonsyns = filter(dietunique, nonsyn == "nonsyn" & n > 1) %>% ungroup() %>% unique() %>% droplevels() 
nonsyns_smol = select(nonsyns,ntvar,aavar,diet,inf_route,n) %>% droplevels()
write.csv(nonsyns_smol, "nonsyns.csv")

nonsyns_dietunique = filter(dietunique, nonsyn == "nonsyn" & n > 1) %>% 
  ungroup() %>% 
  select(diet,ntvar,aavar,n) %>%
  unique() %>%
  arrange(desc(n))

write.table(nonsyns_dietunique, "nonsyns_dietunique.csv", sep = ",", row.names = FALSE)

dNdS analysis of de novo, diet unique genes

# by ferret
dNdS_denovo_ferret = dietunique %>% 
  ungroup() %>% 
  group_by(ferretID,DPI,diet,inf_route) %>% 
  count(nonsyn)

dNdS_denovo_ferret = pivot_wider(dNdS_denovo_ferret,names_from = nonsyn, values_from = n)
dNdS_denovo_ferret = select(dNdS_denovo_ferret, ferretID,DPI,nonsyn,syn)
Adding missing grouping variables: `diet`, `inf_route`
dNdS_denovo_ferret$dNdS = paste0(dNdS_denovo_ferret$nonsyn / dNdS_denovo_ferret$syn)
dNdS_denovo_ferret$dNdS = as.numeric(dNdS_denovo_ferret$dNdS)
Warning: NAs introduced by coercion
dNdS_denovo_ferret_plot = ggplot(dNdS_denovo_ferret, aes(x = DPI, y = dNdS, color = ferretID)) +
  geom_point() +
  geom_line(aes(group = ferretID)) +
  facet_grid(~diet+inf_route) +
  PlotTheme1 
print(dNdS_denovo_ferret_plot)
ggsave("dNdS_denovo_ferret.pdf", dNdS_denovo_ferret_plot, path = savedir)
Saving 7.29 x 4.51 in image

Which ferrets have more than one de novo?

ggplot(dietunique, aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet)) +
  facet_grid(~segment) +
  PlotTheme1 +
  DietcolScale


ggplot(filter(dietunique, n >1), aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet)) +
  facet_grid(~segment) +
  PlotTheme1 +
  DietcolScale

SNVs shared between diet groups

shared = ferunique %>% 
  filter(ntvar %in% o_var) %>% 
  filter(ntvar %in% l_var) %>% 
  unique()
shared$ferretID_var = paste0(shared$ferretID,"_",shared$ntvar)

repeats_shared = shared %>% 
  group_by(ntvar,ferretID) %>% 
  tally() %>%
  group_by(ntvar) %>%
  tally()
# this is to make sure I'm not repeatedly counting a variant found in one ferret but multiple days 

shared = merge(shared, repeats_shared, by = c("ntvar")) %>% unique()


SharedPlot = ggplot(shared, 
                    aes(x = ntpos,
                        y = factor(segment, levels = c('H9N2_NS','H9N2_MP','H9N2_NA','H9N2_NP','H9N2_HA','H9N2_PA','H9N2_PB1','H9N2_PB2')))) +
  geom_point(aes(size = n, color = nonsyn)) +
  geom_text(data = filter(shared, n > 4, nonsyn == "nonsyn"), aes(label = aavar, vjust = 2, hjust = 0.5)) +
  ggtitle("Number of samples containing each variant - Shared between diet groups") +
  ylab("Segment") +
  xlab("Nucleotide Position") +
  PlotTheme1
print(SharedPlot)
ggsave(SharedPlot, filename = "SegmentSNVPlot_DietShared.pdf", path = savedir)
Saving 7.29 x 4.51 in image

Extracting common nonsynonymous variants shared between diet groups

nonsyns_shared = filter(shared, nonsyn == "nonsyn" & n > 1) %>% 
  ungroup() %>% 
  select(ntvar,aavar,n) %>%
  unique() %>%
  arrange(desc(n))

write.table(nonsyns_shared, "nonsyns_shared.csv", sep = ",", row.names = FALSE)

Is there a difference in how often these variants are found in obese v lean ferrets?

shared_vars = group_by(shared, ntvar, diet) %>% tally() 

ggplot(shared_vars, aes(x = ntvar, y = n, fill = diet)) +
geom_col(position = "dodge") + 
#facet_grid(~inf_route) +
PlotTheme1 +
DietcolScale_fill


diff_shared_vars = group_by(shared, ntvar, diet) %>% 
  tally() %>% 
  pivot_wider(names_from = diet, values_from = n) %>% 
  mutate(diff = abs(Obese - Lean)) %>% 
  filter(diff > 2) %>%
  pivot_longer(cols = c("Lean", "Obese"), names_to = c("diet"))
  
ggplot(diff_shared_vars, aes(x = ntvar, y = value, fill = diet)) +
geom_col(position = "dodge") +
#facet_grid(~inf_route) +
PlotTheme1 +
DietcolScale_fill

Is there a difference in AF of the variants found in obese and lean ferrets?

ggplot(shared, aes(x = minorfreq, fill = diet)) +
  geom_histogram(binwidth = 0.01) +
  PlotTheme1 +
  facet_grid(inf_route~diet) +
  DietcolScale_fill


o = filter(ferunique, inf_route == "Index" & diet == "Obese")
l = filter(ferunique, inf_route == "Index" & diet == "Lean")
t.test(o$minorfreq, l$minorfreq)

    Welch Two Sample t-test

data:  o$minorfreq and l$minorfreq
t = 2.6927, df = 375.31, p-value = 0.007405
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.002269461 0.014555720
sample estimates:
 mean of x  mean of y 
0.03563127 0.02721868 
#not significantly different
obese_index = filter(ferunique, diet == "Obese" & inf_route == "Index") %>% ungroup()
lean_index = filter(ferunique, diet == "Lean" & inf_route == "Index") %>% ungroup()
t.test(obese_index$minorfreq, lean_index$minorfreq)

    Welch Two Sample t-test

data:  obese_index$minorfreq and lean_index$minorfreq
t = 2.6927, df = 375.31, p-value = 0.007405
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.002269461 0.014555720
sample estimates:
 mean of x  mean of y 
0.03563127 0.02721868 
# means are not different

obese_contact = filter(ferunique, diet == "Obese" & inf_route == "Contact") %>% ungroup()
lean_contact = filter(ferunique, diet == "Lean" & inf_route == "Contact") %>% ungroup()
t.test(obese_contact$minorfreq, lean_contact$minorfreq)

    Welch Two Sample t-test

data:  obese_contact$minorfreq and lean_contact$minorfreq
t = 0.29049, df = 278.93, p-value = 0.7717
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.01242310  0.01672443
sample estimates:
 mean of x  mean of y 
0.05858870 0.05643803 
# means are not different

# QQ_Plot: compares the quantiles of two distributions, x =y suggests they are drawn from the same distribution
qqnorm(obese_index$minorfreq, main = "Obese Index - Test of Normal Distribution")

qqnorm(lean_index$minorfreq, main = "Lean Index - Test of Normal Distribution")

# neither distribution is normal
qqplot(obese_index$minorfreq,lean_index$minorfreq, xlab = "Obese Index", ylab = "Lean Index")


qqnorm(obese_contact$minorfreq, main = "Obese Contact - Test of Normal Distribution")

qqnorm(lean_contact$minorfreq, main = "Lean Contact - Test of Normal Distribution")

# neither distribution is normal
qqplot(obese_contact$minorfreq,lean_contact$minorfreq, xlab = "Obese Contact", ylab = "Lean Contact")


# Mann-Whitney-Wilcox test (Mann-Whitney U test): samples are not normally distributed and independent of each other
wilcox.test(obese_index$minorfreq,lean_index$minorfreq)

    Wilcoxon rank sum test with continuity correction

data:  obese_index$minorfreq and lean_index$minorfreq
W = 54497, p-value = 0.02064
alternative hypothesis: true location shift is not equal to 0
wilcox.test(obese_contact$minorfreq,lean_contact$minorfreq)

    Wilcoxon rank sum test with continuity correction

data:  obese_contact$minorfreq and lean_contact$minorfreq
W = 16628, p-value = 0.06768
alternative hypothesis: true location shift is not equal to 0
# distributions are not different

# Kolmogorov-Smirnov test: samples are not normally distributed and independent of each other
# "sensitive to differences in location and shape of the empirical CDFs of the two samples"
ks.test(obese_index$minorfreq,lean_index$minorfreq)

    Asymptotic two-sample Kolmogorov-Smirnov test

data:  obese_index$minorfreq and lean_index$minorfreq
D = 0.10492, p-value = 0.06703
alternative hypothesis: two-sided
ks.test(obese_contact$minorfreq,lean_contact$minorfreq)

    Asymptotic two-sample Kolmogorov-Smirnov test

data:  obese_contact$minorfreq and lean_contact$minorfreq
D = 0.16702, p-value = 0.01719
alternative hypothesis: two-sided
# distributions are not different
---
title: "R Notebook"
output: html_notebook
---

```{r}
library("tidyr")
library('ggplot2')
library('dplyr')
library("glue")
library('ggVennDiagram')

wkdir = "~/Desktop/GitHub/Obesity/NewExtractions/H9N2/timo_0.01"
setwd(wkdir)
savedir = "~/Desktop/GitHub/Obesity/NewExtractions/H9N2/timo_0.01/Output_Figures"

source("~/Desktop/GitHub/Obesity/NewExtractions/H9N2/FD_functions.R")
```

```{r}
diet = c("Obese","Lean","Control")
dietColors = c("#FF9933","#66CCFF","#606060")
names(dietColors) = diet
DietcolScale_fill <- scale_fill_manual(name = "grp",values = dietColors)
DietcolScale <- scale_colour_manual(name = "grp",values = dietColors)
```

Specifying thresholds and plotting variables
```{r}
cov_cut = 200
freq_cut = 0.01
pvalcut  = 0.05

ntlist = c("A","C","G","T")
SEGMENTS = c('H9N2_PB2','H9N2_PB1','H9N2_PA','H9N2_HA','H9N2_NP','H9N2_NA','H9N2_MP','H9N2_NS')
```

#Loading metadata
This includes titer and Ct values when applicable. ND indicates qPCR was run with a negative result; 0 indicates plaque assay or HAI was run with a negative result. NA for any values indicate that data was missing. Sacrificed indicates there was no data at that time point because the ferret had already been sacrficied for pathology. 
```{r}
metafile = metafile = "~/Desktop/GitHub/Obesity/NewExtractions/H9N2/H9_Metadata.csv"

meta = read.csv(file=metafile,header=T,sep=",",na.strings = c(''))
meta = filter(meta, resequenced == "yes")

meta$Ct_Mgene = as.numeric(meta$Ct_Mgene)
meta$titer = as.numeric(meta$titer)
meta$log10_titer = as.numeric(meta$log10_titer)

meta$inf_route = factor(meta$inf_route, levels = c("Index","Contact","Aerosol","Control"))
```

Loading in coverage file & segment size information
```{r}
cov = read.csv("./avg_coverage/H9N2.coverage.csv", header = TRUE, sep = ",")

seg_sizes = "../SegmentSize.csv"
sizes = read.csv(file=seg_sizes,header=T,sep=",",na.strings = c(''))
GenomeSize = (sizes %>% filter(segment == 'H9N2_GENOME'))$SegmentSize

cov$segment = factor(cov$segment, levels = SEGMENTS)
```

Checking if data passes thresholds
```{r}
cov_check = CoverageAcross(cov,cov_cut,40,sizes, wkdir)
```

Merging coverage check info with the rest of the metadata
```{r}
meta = merge(meta, cov_check, by.x = c("sample"), by.y = c("name"), all.y = TRUE)

nrow(meta)
count(meta,quality)
```

Loading in variant files
```{r}
varfile = "./varfiles/H9N2.VariantsOnly.0.01.200.csv"

# read and rearrange the data
vars = read.csv(file=varfile,header=T,sep=",",na.strings = c(''))
vars$name = vars$sample
```

Rearranging variant dataframe
```{r}
vdf = ArrangeVarWRep(vars)
# already have replicate data in the varfiles from running CompareReps.v2.py script
vdf = vdf[!duplicated(vdf), ] %>% droplevels()
nrow(vdf)
```

Filtering variant df by timo binocheck
```{r}
vdf$binocheck = factor(vdf$binocheck, levels = c("False","R1","R2","True"))
vdf_bino = filter(vdf, binocheck != "False")
vdf_bino = vdf_bino[!duplicated(vdf_bino), ] %>% droplevels()
nrow(vdf_bino)
# this really gets rid of a lot of variants (~1000)
```

Filtering variant df with frequency cutoffs
```{r}
vdf = filter(vdf, minorfreq1 >= freq_cut & 
               minorfreq2 >= freq_cut & 
               minor %in% ntlist &
               major %in% ntlist) %>% 
            droplevels()
# based on MAF study, reps and 0.01% cutoff was best combo
#filter each replicate separately rather than using the average

vdf = vdf[!duplicated(vdf), ] %>% droplevels()
nrow(vdf)
# does not eliminate any variants here
```

Adding metadata
```{r}
vdf = merge(vdf,meta, by = c("sample","segment"))
vdf = vdf[!duplicated(vdf), ] %>% droplevels()

vdf$segment = factor(vdf$segment, levels = SEGMENTS)

vdf = filter(vdf, inf_route == "Index" | inf_route == "Contact" | inf_route == "Control")
# ignoring aerosol for now
```

```{r}
vdf = filter(vdf, quality == "good")
vdf = vdf[!duplicated(vdf), ] %>% droplevels()

good_names = c(levels(factor(vdf$sample)))
```

```{r}
transmission_info = "/Users/marissaknoll/Desktop/GitHub/Obesity/NewExtractions/H9N2/TransmissionPairs.csv"
pairs = read.csv(transmission_info, header = T)
```

```{r}
con_change = filter(vdf, stocknt != major) %>%
  filter(major %in% ntlist)
con_change = con_change[!duplicated(con_change), ]
con_change$ntvar = paste0(con_change$ferretID,"_",con_change$segment,"_",
                        con_change$major,"_",con_change$ntpos,"_",con_change$minor)
consensus = unique(con_change$ntvar)
length(consensus)
```

```{r}
vdf$ntvar = paste0(vdf$ferretID,"_",vdf$segment,"_",vdf$major,"_",vdf$ntpos,"_",vdf$minor)

minorvdf = filter(vdf, !(ntvar %in% consensus)) %>% unique()
nrow(vdf) - nrow(minorvdf)
```

SNV location plots
```{r}
SNVLocation = ggplot(minorvdf, aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet, shape = cohort)) +
  facet_grid(inf_route~segment) +
  PlotTheme1 +
  DietcolScale
print(SNVLocation)
ggsave(SNVLocation, file = "SNVLocation.pdf", path = savedir)
# ferret 1787 doesn't have any variants??
```

```{r}
minorvdf$ntvar = paste0(minorvdf$segment,"_",minorvdf$major,minorvdf$ntpos,minorvdf$minor)

# Comparing to SNVs found in the stock

F17_stock = filter(minorvdf, DPI == "Stock", cohort == "F17") 
F17_stock_ntvar = unique(F17_stock$ntvar)
W17_stock = filter(minorvdf, DPI == "Stock", cohort == "W17")
W17_stock_ntvar = unique(W17_stock$ntvar)
Sm18_stock = filter(minorvdf, DPI == "Stock", cohort == "Sm18")
Sm18_stock_ntvar = unique(Sm18_stock$ntvar)
Sp19_stock = filter(minorvdf, DPI == "Stock", cohort == "Sp19")
Sp19_stock_ntvar = unique(Sp19_stock$ntvar)
Sp20_stock = filter(minorvdf, DPI == "Stock", cohort == "Sp20")
Sp20_stock_ntvar = unique(Sp20_stock$ntvar)

F17_ferret = filter(minorvdf , cohort == "F17", inf_route != "Control")
F17_ferret_ntvar = unique(F17_ferret$ntvar)
W17_ferret = filter(minorvdf ,cohort == "W17", inf_route != "Control")
W17_ferret_ntvar = unique(W17_ferret$ntvar)
Sm18_ferret = filter(minorvdf ,cohort == "Sm18", inf_route != "Control")
Sm18_ferret_ntvar = unique(Sm18_ferret$ntvar)
Sp19_ferret = filter(minorvdf ,cohort == "Sp19", inf_route != "Control")
Sp19_ferret_ntvar = unique(Sp19_ferret$ntvar)
Sp20_ferret = filter(minorvdf ,cohort == "Sp20", inf_route != "Control")
Sp20_ferret_ntvar = unique(Sp20_ferret$ntvar)
```

```{r}
F17_shared = F17_ferret %>% filter(ntvar %in% F17_stock_ntvar) %>% filter((ntvar %in% F17_ferret_ntvar)) %>% unique()
F17_denovo = F17_ferret %>% filter((ntvar %in% F17_ferret_ntvar)) %>% filter(!(ntvar %in% F17_stock_ntvar)) %>% unique()

W17_shared = W17_ferret %>% filter(ntvar %in% W17_stock_ntvar) %>% filter((ntvar %in% W17_ferret_ntvar)) %>% unique()
W17_denovo = W17_ferret %>% filter((ntvar %in% W17_ferret_ntvar)) %>% filter(!(ntvar %in% W17_stock_ntvar)) %>% unique()

Sm18_shared = Sm18_ferret %>% filter(ntvar %in% Sm18_stock_ntvar) %>% filter((ntvar %in% Sm18_ferret_ntvar)) %>% unique()
Sm18_denovo = Sm18_ferret %>% filter((ntvar %in% Sm18_ferret_ntvar)) %>% filter(!(ntvar %in% Sm18_stock_ntvar)) %>% unique()

Sp19_shared = Sp19_ferret %>% filter(ntvar %in% Sp19_stock_ntvar) %>% filter((ntvar %in% Sp19_ferret_ntvar)) %>% unique()
Sp19_denovo = Sp19_ferret %>% filter((ntvar %in% Sp19_ferret_ntvar)) %>% filter(!(ntvar %in% Sp19_stock_ntvar)) %>% unique()

Sp20_shared = Sp20_ferret %>% filter(ntvar %in% Sp20_stock_ntvar) %>% filter((ntvar %in% Sp20_ferret_ntvar)) %>% unique()
Sp20_denovo = Sp20_ferret %>% filter((ntvar %in% Sp20_ferret_ntvar)) %>% filter(!(ntvar %in% Sp20_stock_ntvar)) %>% unique()
```

```{r}
stock_shared = rbind(F17_shared, W17_shared, Sm18_shared, Sp19_shared, Sp20_shared)
stock_shared$aavar = paste0(stock_shared$majoraa,stock_shared$aapos,stock_shared$minoraa)

ferunique = rbind(F17_denovo, W17_denovo, Sm18_denovo, Sp19_denovo, Sp20_denovo)
ferunique$aavar = paste0(ferunique$majoraa,ferunique$aapos,ferunique$minoraa)
```

SNV Location compared to stock
```{r}
StockSharedPlot = ggplot(stock_shared, aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet, shape = cohort), size = 2) +
  facet_grid(inf_route~segment, drop = FALSE) +
  PlotTheme1 +
  DietcolScale +
  ggtitle("SNVs found in stock")
print(StockSharedPlot)
ggsave(StockSharedPlot, file = "StockSharedPlot.pdf", height = 30, width = 15, path = savedir)

FerUniquePlot = ggplot(ferunique, aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet)) +
  facet_grid(inf_route~segment) +
  PlotTheme1 +
  DietcolScale +
  ggtitle("SNVs not found in stock")
print(FerUniquePlot)
ggsave(FerUniquePlot, file = "FerUniquePlot.pdf", path = savedir)
```

Venn diagram of obese and lean de novo SNVs
```{r}
o_var = filter(ferunique, diet == "Obese") 
o_var = unique(o_var$ntvar)

l_var = filter(ferunique, diet == "Lean") 
l_var = unique(l_var$ntvar)

diet_var <- list(Obese = o_var, Lean = l_var)

DietUniqueSNVS = ggVennDiagram(diet_var)
print(DietUniqueSNVS)
ggsave(DietUniqueSNVS, file = "DietUniqueSNVS.pdf", path = savedir)
```

Obese- and lean-specific SNVs
```{r}
lean = ferunique %>% 
  filter(ntvar %in% l_var) %>% 
  filter(!(ntvar %in% o_var)) %>% 
  unique()

lean$ferretID_var = paste0(lean$ferretID,"_",lean$ntvar)

repeats_lean = lean %>% 
  group_by(ntvar,ferretID) %>% 
  tally() %>%
  group_by(ntvar) %>%
  tally() %>% unique()

lean = merge(lean, repeats_lean, by = c("ntvar")) %>% unique()

obese = ferunique %>% 
  filter(ntvar %in% o_var) %>% 
  filter(!(ntvar %in% l_var)) %>%
  unique()

obese$ferretID_var = paste0(obese$ferretID,"_",obese$ntvar)

repeats_obese = obese %>% 
  group_by(ntvar,ferretID) %>% 
  tally() %>%
  group_by(ntvar) %>%
  tally() %>%
  unique()

obese = merge(obese, repeats_obese, by = c("ntvar")) %>% unique()
```

Determining if diet-unique shared variants are transmitted
```{r}
dietunique = rbind(lean,obese) %>% unique()
dietunique = merge(dietunique, pairs, by = c("ferretID"))

shared = filter(dietunique, n == 2)
t = unique(shared$ntvar)

transmitted = data.frame()

for(i in t){
  print(i)
  df = filter(shared, ntvar == i)
  df1 = group_by(df,pair_numbers) %>% tally()
  # here a 2 means that the two ferrets are in the same transmission pair and a 1 indicates different transmission pairs
  df2 = merge(df, df1, by = c("pair_numbers"))
  # add this information back into the dataframe
  df2$transmission = df2$n.y
  transmitted = rbind(transmitted, df2)
}

#formatting stuff
notshared = filter(dietunique, n == 1)
notshared$transmission = 0
transmitted$n = transmitted$n.x
transmitted = transmitted %>% select(!(n.x)) %>% select(!(n.y))

dietunique = rbind(notshared, transmitted)
dietunique$transmission = as.character(dietunique$transmission)
```

```{r}
DietUnique = ggplot(dietunique, 
                    aes(x = ntpos,
                        y = factor(segment, levels = c('H9N2_NS','H9N2_MP','H9N2_NA','H9N2_NP','H9N2_HA','H9N2_PA','H9N2_PB1','H9N2_PB2')))) +
  geom_point(aes(color = nonsyn, size = n)) + 
  geom_text(data = filter(dietunique, n == 2, nonsyn == "nonsyn"), aes(label = aavar, vjust = 2, hjust = 0.5)) +
  ggtitle("Number of samples containing each variant - diet specific") +
  facet_grid(STRAIN~diet) +
  ylab("Segment") +
  xlab("Nucleotide Position") +
  PlotTheme1
print(DietUnique)
ggsave(DietUnique, filename = "SegmentSNVPlot_DietUnqique.pdf", path = savedir, width = 10, height = 7)
```

```{r}
# make new version of this figure, separating out transmission v independent ferrets
DietUnique_Transmission = ggplot(filter(dietunique, n > 1, nonsyn != "syn"), 
                             aes(x = ntpos, 
                                 y = factor(segment, levels = c('H9N2_NS','H9N2_MP','H9N2_NA','H9N2_NP','H9N2_HA','H9N2_PA','H9N2_PB1','H9N2_PB2')))) +
  geom_point(aes(color = transmission, size = 2, shape = transmission)) + 
  ggtitle("Number of samples containing each variant - diet specific") +
  xlab("Nucleotide position") +
  ylab("Segment") +
  facet_grid(diet~inf_route) +
  PlotTheme1
print(DietUnique_Transmission)
ggsave(DietUnique_Transmission, file = "DietUnique_Transmission.pdf", width = 7, height = 5, path = savedir)
```

Pulling out repeated nonsynonymous mutations
```{r}
nonsyns = filter(dietunique, nonsyn == "nonsyn" & n > 1) %>% ungroup() %>% unique() %>% droplevels() 
nonsyns_smol = select(nonsyns,ntvar,aavar,diet,inf_route,n) %>% droplevels()
write.csv(nonsyns_smol, "nonsyns.csv")

nonsyns_dietunique = filter(dietunique, nonsyn == "nonsyn" & n > 1) %>% 
  ungroup() %>% 
  select(diet,ntvar,aavar,n) %>%
  unique() %>%
  arrange(desc(n))

write.table(nonsyns_dietunique, "nonsyns_dietunique.csv", sep = ",", row.names = FALSE)
```

dNdS analysis of de novo, diet unique genes
```{r}
# by ferret
dNdS_denovo_ferret = dietunique %>% 
  ungroup() %>% 
  group_by(ferretID,DPI,diet,inf_route) %>% 
  count(nonsyn)

dNdS_denovo_ferret = pivot_wider(dNdS_denovo_ferret,names_from = nonsyn, values_from = n)
dNdS_denovo_ferret = select(dNdS_denovo_ferret, ferretID,DPI,nonsyn,syn)
dNdS_denovo_ferret$dNdS = paste0(dNdS_denovo_ferret$nonsyn / dNdS_denovo_ferret$syn)
dNdS_denovo_ferret$dNdS = as.numeric(dNdS_denovo_ferret$dNdS)

dNdS_denovo_ferret_plot = ggplot(dNdS_denovo_ferret, aes(x = DPI, y = dNdS, color = ferretID)) +
  geom_point() +
  geom_line(aes(group = ferretID)) +
  facet_grid(~diet+inf_route) +
  PlotTheme1 
print(dNdS_denovo_ferret_plot)
ggsave("dNdS_denovo_ferret.pdf", dNdS_denovo_ferret_plot, path = savedir)
```


Which ferrets have more than one de novo?
```{r}
ggplot(dietunique, aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet)) +
  facet_grid(~segment) +
  PlotTheme1 +
  DietcolScale

ggplot(filter(dietunique, n >1), aes(x = ntpos, y = ferretID)) +
  geom_point(aes(color = diet)) +
  facet_grid(~segment) +
  PlotTheme1 +
  DietcolScale
```
SNVs shared between diet groups
```{r}
shared = ferunique %>% 
  filter(ntvar %in% o_var) %>% 
  filter(ntvar %in% l_var) %>% 
  unique()
shared$ferretID_var = paste0(shared$ferretID,"_",shared$ntvar)

repeats_shared = shared %>% 
  group_by(ntvar,ferretID) %>% 
  tally() %>%
  group_by(ntvar) %>%
  tally()
# this is to make sure I'm not repeatedly counting a variant found in one ferret but multiple days 

shared = merge(shared, repeats_shared, by = c("ntvar")) %>% unique()

SharedPlot = ggplot(shared, 
                    aes(x = ntpos,
                        y = factor(segment, levels = c('H9N2_NS','H9N2_MP','H9N2_NA','H9N2_NP','H9N2_HA','H9N2_PA','H9N2_PB1','H9N2_PB2')))) +
  geom_point(aes(size = n, color = nonsyn)) +
  geom_text(data = filter(shared, n > 4, nonsyn == "nonsyn"), aes(label = aavar, vjust = 2, hjust = 0.5)) +
  ggtitle("Number of samples containing each variant - Shared between diet groups") +
  ylab("Segment") +
  xlab("Nucleotide Position") +
  PlotTheme1
print(SharedPlot)
ggsave(SharedPlot, filename = "SegmentSNVPlot_DietShared.pdf", path = savedir, height = 10, width = 9)
```

Extracting common nonsynonymous variants shared between diet groups
```{r}
nonsyns_shared = filter(shared, nonsyn == "nonsyn" & n > 1) %>% 
  ungroup() %>% 
  select(ntvar,aavar,n) %>%
  unique() %>%
  arrange(desc(n))

write.table(nonsyns_shared, "nonsyns_shared.csv", sep = ",", row.names = FALSE)
```

Is there a difference in how often these variants are found in obese v lean ferrets?
```{r}
shared_vars = group_by(shared, ntvar, diet) %>% tally() 

ggplot(shared_vars, aes(x = ntvar, y = n, fill = diet)) +
geom_col(position = "dodge") + 
#facet_grid(~inf_route) +
PlotTheme1 +
DietcolScale_fill

diff_shared_vars = group_by(shared, ntvar, diet) %>% 
  tally() %>% 
  pivot_wider(names_from = diet, values_from = n) %>% 
  mutate(diff = abs(Obese - Lean)) %>% 
  filter(diff > 2) %>%
  pivot_longer(cols = c("Lean", "Obese"), names_to = c("diet"))
  
ggplot(diff_shared_vars, aes(x = ntvar, y = value, fill = diet)) +
geom_col(position = "dodge") +
#facet_grid(~inf_route) +
PlotTheme1 +
DietcolScale_fill
```


Is there a difference in AF of the variants found in obese and lean ferrets?
```{r}
ggplot(shared, aes(x = minorfreq, fill = diet)) +
  geom_histogram(binwidth = 0.01) +
  PlotTheme1 +
  facet_grid(inf_route~diet) +
  DietcolScale_fill

o = filter(ferunique, inf_route == "Index" & diet == "Obese")
l = filter(ferunique, inf_route == "Index" & diet == "Lean")
t.test(o$minorfreq, l$minorfreq)
#not significantly different
```

```{r}
obese_index = filter(ferunique, diet == "Obese" & inf_route == "Index") %>% ungroup()
lean_index = filter(ferunique, diet == "Lean" & inf_route == "Index") %>% ungroup()
t.test(obese_index$minorfreq, lean_index$minorfreq)
# means are not different

obese_contact = filter(ferunique, diet == "Obese" & inf_route == "Contact") %>% ungroup()
lean_contact = filter(ferunique, diet == "Lean" & inf_route == "Contact") %>% ungroup()
t.test(obese_contact$minorfreq, lean_contact$minorfreq)
# means are not different

# QQ_Plot: compares the quantiles of two distributions, x =y suggests they are drawn from the same distribution
qqnorm(obese_index$minorfreq, main = "Obese Index - Test of Normal Distribution")
qqnorm(lean_index$minorfreq, main = "Lean Index - Test of Normal Distribution")
# neither distribution is normal
qqplot(obese_index$minorfreq,lean_index$minorfreq, xlab = "Obese Index", ylab = "Lean Index")

qqnorm(obese_contact$minorfreq, main = "Obese Contact - Test of Normal Distribution")
qqnorm(lean_contact$minorfreq, main = "Lean Contact - Test of Normal Distribution")
# neither distribution is normal
qqplot(obese_contact$minorfreq,lean_contact$minorfreq, xlab = "Obese Contact", ylab = "Lean Contact")

# Mann-Whitney-Wilcox test (Mann-Whitney U test): samples are not normally distributed and independent of each other
wilcox.test(obese_index$minorfreq,lean_index$minorfreq)
wilcox.test(obese_contact$minorfreq,lean_contact$minorfreq)
# distributions are not different

# Kolmogorov-Smirnov test: samples are not normally distributed and independent of each other
# "sensitive to differences in location and shape of the empirical CDFs of the two samples"
ks.test(obese_index$minorfreq,lean_index$minorfreq)
ks.test(obese_contact$minorfreq,lean_contact$minorfreq)
# distributions are not different
```

